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Title
Human activity recognition adapted to the type of movement
Authors
Keywords
Human movements characteristics, Type of movements, Deep learning, Convolutional neural networks, Fast Fourier Transform, Intra-window LSTM
Journal
COMPUTERS & ELECTRICAL ENGINEERING
Volume 88, Issue -, Pages 106822
Publisher
Elsevier BV
Online
2020-09-02
DOI
10.1016/j.compeleceng.2020.106822
References
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Related references
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- (2019) Mingyang Wang et al. DIGITAL SIGNAL PROCESSING
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- HuMAn: Complex Activity Recognition with Multi-modal Multi-positional Body Sensing
- (2018) Pratool Bharti et al. IEEE TRANSACTIONS ON MOBILE COMPUTING
- Deep learning for sensor-based activity recognition: A Survey
- (2018) Jindong Wang et al. PATTERN RECOGNITION LETTERS
- Probabilistic identification of sit-to-stand and stand-to-sit with a wearable sensor
- (2018) Uriel Martinez-Hernandez et al. PATTERN RECOGNITION LETTERS
- Deep Recurrent Neural Networks for Human Activity Recognition
- (2017) Abdulmajid Murad et al. SENSORS
- Adaptive sliding window segmentation for physical activity recognition using a single tri-axial accelerometer
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- Human activity recognition with smartphone sensors using deep learning neural networks
- (2016) Charissa Ann Ronao et al. EXPERT SYSTEMS WITH APPLICATIONS
- Frequency features and GMM-UBM approach for gait-based person identification using smartphone inertial signals
- (2016) Rubén San-Segundo et al. PATTERN RECOGNITION LETTERS
- Deep Convolutional and LSTM Recurrent Neural Networks for Multimodal Wearable Activity Recognition
- (2016) Francisco Ordóñez et al. SENSORS
- Segmenting human activities based on HMMs using smartphone inertial sensors
- (2016) Rubén San-Segundo et al. Pervasive and Mobile Computing
- High dimensional low sample size activity recognition using geometric classifiers
- (2015) Muhammad Shahzad Cheema et al. DIGITAL SIGNAL PROCESSING
- A tutorial on human activity recognition using body-worn inertial sensors
- (2014) Andreas Bulling et al. ACM COMPUTING SURVEYS
- Window Size Impact in Human Activity Recognition
- (2014) Oresti Banos et al. SENSORS
- Recognizing Daily and Sports Activities in Two Open Source Machine Learning Environments Using Body-Worn Sensor Units
- (2013) B. Barshan et al. COMPUTER JOURNAL
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